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Heterogeneity within and across Pediatric Pulmonary Infections: From Bipartite Networks to At-Risk Subphenotypes

Suresh K Bhavnani1, Bryant Dang1, Maria Caro1

  • 1Inst. for Translational Sciences, Inst. for Human Infections and Immunity, Univ. of Texas Medical Branch, Galveston, TX.

AMIA Joint Summits on Translational Science Proceedings. AMIA Joint Summits on Translational Science
|February 27, 2015
PubMed

Insights

Severe influenza (flu) and respiratory syncytial virus (RSV) infections in young children may be linked to distinct genetic and immune responses. Identifying these host-specific patterns could lead to targeted therapies for severe pediatric respiratory infections.

Area of Science:

  • Pediatric Infectious Diseases
  • Genomics
  • Immunology

Background:

  • Influenza (flu) and respiratory syncytial virus (RSV) are common in children under two.
  • A subset of children hospitalized with flu or RSV lack identifiable clinical risk factors for severe disease.
  • Inherent host-specific genetic and immune mechanisms in at-risk children remain poorly understood.

Purpose of the Study:

  • To identify host-specific genetic and immune mechanisms in children hospitalized with flu or RSV.
  • To analyze differentially-expressed genes in a whole genome-wide case-control study.
  • To utilize bipartite networks for analyzing gene expression patterns.

Main Methods:

  • Secondary analysis of a whole genome-wide case-control study.
  • Application of bipartite networks to gene expression data.
  • Identification of statistically significant, differentially-expressed genes.

Main Results:

  • Three distinct clusters of cases were identified for both flu and RSV infections.
  • Clusters represent varying levels of immune responsiveness: hyper-responsive (core), medium-responsive (periphery), and normal-responsive (control-like).
  • Gene expression signatures differentiated these responsiveness levels.

Conclusions:

  • The study provides testable hypotheses for distinct pediatric subphenotypes of flu and RSV.
  • Bipartite gene expression networks can reveal complex patterns related to subphenotypes.
  • Findings offer insights for developing targeted therapeutics for severe pediatric respiratory infections.

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